OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Mimouna, Amira; Alouani, Ihsen; Khalifa, Anouar Ben; Hillali, Yassin El; Taleb‐Ahmed, Abdelmalik; Menhaj, Atika; Ouahabi, Abdeldjalil; Amara, Najoua Essoukri Ben

doi:10.3390/electronics9040560

Cited by 34 publications

(15 citation statements)

References 45 publications

(55 reference statements)

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“…The importance of creating face recognition datasets is essential, first, for security-related applications, and second, to allow the development and validation of methods based on deep learning in 3D facial recognition. Thus, even in specific fields such as autonomous vehicles, a multimodal database has recently been proposed [169] and can be supplemented by 3D facial expression recognition.…”

Section: Three-dimensional Face Recognition Databasesmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

et al. 2020

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Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

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Section: Three-dimensional Face Recognition Databasesmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

et al. 2020

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“…However, this dataset contains radar data with sparsely populated 2-D radar targets. The very recent public dataset (OLIMP [ 53 ]) tailored for VRU detection and tracking incorporates measurements from both camera and ultra wide-band radar sensors. The captured radar data, however, is incomplete as it lacks dense azimuth information which makes it incompatible to our method.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Cooperative Multi-Sensor Tracking of Vulnerable Road Users in the Presence of Missing Detections

Dimitrievski

Hamme

Veelaert

et al. 2020

Sensors

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This paper presents a vulnerable road user (VRU) tracking algorithm capable of handling noisy and missing detections from heterogeneous sensors. We propose a cooperative fusion algorithm for matching and reinforcing of radar and camera detections using their proximity and positional uncertainty. The belief in the existence and position of objects is then maximized by temporal integration of fused detections by a multi-object tracker. By switching between observation models, the tracker adapts to the detection noise characteristics making it robust to individual sensor failures. The main novelty of this paper is an improved imputation sampling function for updating the state when detections are missing. The proposed function uses a likelihood without association that is conditioned on the sensor information instead of the sensor model. The benefits of the proposed solution are two-fold: firstly, particle updates become computationally tractable and secondly, the problem of imputing samples from a state which is predicted without an associated detection is bypassed. Experimental evaluation shows a significant improvement in both detection and tracking performance over multiple control algorithms. In low light situations, the cooperative fusion outperforms intermediate fusion by as much as 30%, while increases in tracking performance are most significant in complex traffic scenes.

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“…The methods in this category employ consecutive hidden layers of informationprocessing arranged hierarchically for representation, learning, and classification. They can automatically determine complex non-linear data structures [40]. Zeng et al (2017) [41] proposed a method that uses Deep Convolutional Neural Networks (DCNNs).…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

Adjabi

Ouahabi

Benzaoui

et al. 2021

Sensors

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Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.

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OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Cited by 34 publications

References 45 publications

Past, Present, and Future of Face Recognition: A Review

Past, Present, and Future of Face Recognition: A Review

Cooperative Multi-Sensor Tracking of Vulnerable Road Users in the Presence of Missing Detections

Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

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